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Found 24 papers, 3 papers with code
Cross-Domain Language Modeling: An Empirical Investigation
Transformer encoder models exhibit strong performance in single-domain applications.
Combining Shallow and Deep Representations for Text-Pair Classification
Contemporary methods use fine-tuned transformer encoder semantic representations of the classification token in the text-pair sequence from the transformer’s final layer for class prediction.
Pandemic Literature Search: Finding Information on COVID-19
Finding information related to a pandemic of a novel disease raises new challenges for information seeking and retrieval, as the new information becomes available gradually.
Measuring Similarity of Opinion-bearing Sentences
For many NLP applications of online reviews, comparison of two opinion-bearing sentences is key.
Identifying Health Risks from Family History: A Survey of Natural Language Processing Techniques
In addition to the areas where NLP has successfully been utilised, we also identify the areas where more research is needed to unlock the value of patients' records regarding data collection, task formulation and downstream applications.
Detecting Entities in the Astrophysics Literature: A Comparison of Word-based and Span-based Entity Recognition Methods
Information Extraction from scientific literature can be challenging due to the highly specialised nature of such text.
Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media
Recent studies on domain-specific BERT models show that effectiveness on downstream tasks can be improved when models are pretrained on in-domain data.
Ranked #3 on
Clinical Concept Extraction
on 2010 i2b2/VA
Searching Scientific Literature for Answers on COVID-19 Questions
Finding answers related to a pandemic of a novel disease raises new challenges for information seeking and retrieval, as the new information becomes available gradually.
An Effective Transition-based Model for Discontinuous NER
Unlike widely used Named Entity Recognition (NER) data sets in generic domains, biomedical NER data sets often contain mentions consisting of discontinuous spans.
ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge
We report on our system for textual inference and question entailment in the medical domain for the ACL BioNLP 2019 Shared Task, MEDIQA.
Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection
The introduction of figurative usage detection results in an average improvement of 2. 21% F-score of personal health mention detection, in the case of the feature augmentation-based approach.
A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics
Distributed representations of text can be used as features when training a statistical classifier.
NNE: A Dataset for Nested Named Entity Recognition in English Newswire
Named entity recognition (NER) is widely used in natural language processing applications and downstream tasks.
Using Similarity Measures to Select Pretraining Data for NER
Word vectors and Language Models (LMs) pretrained on a large amount of unlabelled data can dramatically improve various Natural Language Processing (NLP) tasks.
Ranked #1 on
Named Entity Recognition (NER)
on WetLab
Red-faced ROUGE: Examining the Suitability of ROUGE for Opinion Summary Evaluation
Opinion summarisation requires to correctly pair two types of semantic information: (1) aspect or opinion target; and (2) polarity of candidate and reference summaries.
Does Multi-Task Learning Always Help?: An Evaluation on Health Informatics
Multi-Task Learning (MTL) has been an attractive approach to deal with limited labeled datasets or leverage related tasks, for a variety of NLP problems.
Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective
Epidemic intelligence deals with the detection of disease outbreaks using formal (such as hospital records) and informal sources (such as user-generated text on the web) of information.
Shot Or Not: Comparison of NLP Approaches for Vaccination Behaviour Detection
Vaccination behaviour detection deals with predicting whether or not a person received/was about to receive a vaccine.
Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods
Diagnosis autocoding services and research intend to both improve the productivity of clinical coders and the accuracy of the coding.
Concept Extraction to Identify Adverse Drug Reactions in Medical Forums: A Comparison of Algorithms
Our evaluations were performed in a controlled setting on a common corpus which is a collection of medical forum posts annotated with concepts and linked to controlled vocabularies such as MedDRA and SNOMED CT. To our knowledge, our study is the first to systematically examine the effect of popular concept extraction methods in the area of signal detection for adverse reactions.
